Machine Learning Course Research Articles

Delivering a machine learning course on HPC resources

In recent years, proficiency in data science and machine learning (ML) became one of the most requested skills for jobs in both industry and academy. Machine learning algorithms typically require large sets of data to train the models and extensive usage of computing resources, both for training and inference. Especially for deep learning algorithms, training performances can be dramatically improved by exploiting Graphical Processing Units (GPUs). The needed skill set for a data scientist is therefore extremely broad, and ranges from knowledge of ML models to distributed programming on heterogeneous resources. While most of the available training resources focus on ML algorithms and tools such as TensorFlow, we designed a course for doctoral students where model training is tightly coupled with underlying technologies that can be used to dynamically provision resources. Throughout the course, students have access to a dedicated cluster of computing nodes on local premises. A set of libraries and helper functions is provided to execute a parallelized ML task by automatically deploying a Spark driver and several Spark execution nodes as Docker containers. Task scheduling is managed by an orchestration layer (Kubernetes). This solution automates the delivery of the software stack required by a typical ML workflow and enables scalability by allowing the execution of ML tasks, including training, over commodity (i.e. CPUs) or high-performance (i.e. GPUs) resources distributed over different hosts across a network. The adaptation of the same model on OCCAM, the HPC facility at the University of Turin, is currently under development.

Information Mathematics for Machine Learning

This manuscript is a supplement to the machine learning course at JSMP Medical Physics Summer School held in 2019. The idea of Kulbuck-Leibler divergence, a key concept in machine learning, is introduced with mutual information.

AI education matters

Traditionally artificial intelligence (AI) and machine learning (ML) courses are taught at the senior and graduate level in higher-education computer science curricula following the mastery learning strategy, cf. Figure 1. This makes sense, since most AI and ML models and the theory behind them require a substantial understanding of probability and statistics, as well as advanced calculus and matrix algebra. To understand Logistic Regression as a probabilistic classifier performing maximum-likelihood or maximum-a-posteriori estimation, for example, students need to understand joint and conditional probability distributions. In order to derive the back propagation algorithm to train Neural Networks students need to understand partial derivatives and inner and outer tensor products. These are just two of many examples where substantial mathematical background - typically taught at the junior level in a computer science major program - is required. With AI and ML algorithms being used more widely by enterprises across domains, as well as, in applications and services we use in our daily lives, it makes sense to raise awareness about what AI is, what it can and cannot do, and how it is used to solve problems to a broader audience. Very much in the same spirit as the "CS for all" idea (https://www.csforall.org), we have to extend our curricula to include introductory courses to AI and ML on the early undergraduate level (or even in high-school) to expose students to the ideas and working principles of AI technology. One way to achieve this is to introduce the principles of working with data, modeling, and learning through the data science workflow.

Integrating Ethics within Machine Learning Courses

This article establishes and addresses opportunities for ethics integration into Machine-learning (ML) courses. Following a survey of the history of computing ethics and the current need for ethical consideration within ML, we consider the current state of ML ethics education via an exploratory analysis of course syllabi in computing programs. The results reveal that though ethics is part of the overall educational landscape in these programs, it is not frequently a part of core technical ML courses. To help address this gap, we offer a preliminary framework, developed via a systematic literature review, of relevant ethics questions that should be addressed within an ML project. A pilot study with 85 students confirms that this framework helped them identify and articulate key ethical considerations within their ML projects. Building from this work, we also provide three example ML course modules that bring ethical thinking directly into learning core ML content. Collectively, this research demonstrates: (1) the need for ethics to be taught as integrated within ML coursework, (2) a structured set of questions useful for identifying and addressing potential issues within an ML project, and (3) novel course models that provide examples for how to practically teach ML ethics without sacrificing core course content. An additional by-product of this research is the collection and integration of recent publications in the emerging field of ML ethics education.

Исследовательский проект как инструмент обучения методам анализа текста: предсказание класса поста в социальной сети

The article describes a student research project on predicting the class of a post on a social network based on its textual content. The features of the project are discussed as an integral part of the trajectory of teaching data analysis methods, including text analysis methods and tools that are often not included in machine learning courses. The formulation of the problem, the stages of its solution, the sequence of considering new methods as a way for solving students’ problems, as well as the used tool of the R environment are described. The possibilities of expanding the task and its modifications depending on the level of training of students are given.

Educational Advances in Artificial Intelligence

The emergence of massive open online courses has initiated a broad national-wide discussion on higher education practices, models, and pedagogy. Artificial intelligence and machine learning courses were at the forefront of this trend and are also being used to serve personalized, managed content in the back-end systems. Massive open online courses are just one example of the sorts of pedagogical innovations being developed to better teach AI. This column will discuss and share innovative educational approaches that teach or leverage AI and its many subfields, including robotics, machine learning, natural language processing, computer vision, and others at all levels of education (K-12, undergraduate, and graduate levels). In particular, this column will serve the community as a venue to learn about the Symposium on Educational Advances in Artificial Intelligence (EAAI) (colocated with AAAI for the past four years); introductions to innovative pedagogy and best practices for AI and across the computer science curricula; resources for teaching AI, including model AI assignments, software packages, online videos and lectures that can be used in your classroom; topic tutorials introducing a subject to students and researchers with links to articles, presentations, and online materials; and discussion of the use of AI methods in education shaping personalized tutorials, learning analytics, and data mining

Reading frankenstein in an introduction to machine learning course

This assignment requires students to read and present on material from Mary Shelley's Frankenstein; or The Modern Prometheus in an introductory machine learning course. Students create an interesti...

Learning Machine Learning: A Case Study

This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning objectives.

CRCD in machine learning at the University of Central Florida preliminary experiences

We will present work in progress on a Combined Research and Curriculum Development (CRCD) project at the University of Central Florida entitled Advances in Machine Learning in Engineering and Science Education. The project is funded by a grant from the National Science Foundation and involves the development of a curriculum model for the integration of machine learning research into science and engineering disciplines. This is a two-phase project. The first phase, which is where we are now, involves the integration of such modules into the lower level science and engineering courses, while the second phase involves the introduction of upper level machine learning courses.

Genetic programming applied to Othello

In this paper we describe and analyze a three week assignment that was given in a Machine Learning course at Columbia University. The assignment presented students with an introduction to machine learning research. The assignment required students to apply Genetic Programming to evolve algorithms that play the board game Othello. The students were provided with an implemented experimental approach as a starting point. The students were required to perform their own experimental modifications corresponding to research issues in machine learning. The results of student experiments were good both in terms of research and in terms of student learning. All relevant code, documentation and information about GPOthello is available at the following url: http://www.cs.columbia.edu/~evs/ml/othello.html.